Background of the Technology
Travel and travel-related expenses constitute a large expense facing organized entities. There is a need in the art to better analyze, monitor, and control these expenses, while maintaining accuracy and increasing worker productivity. For example, reducing time spent on expense reports allows workers to spend more time on core job functions.
Automated expense management systems have moved the traditional paper-based travel expense reporting process on-line. Credit card data feeds contain credit card transaction information. Travel data feeds also exist that contain information about travel reservations obtained from either the travel agency or from a Global Distribution System (GDS) vendor (e.g., Apollo, Sabre, Galileo, Amadeus, Worldspan). Although it is useful to import credit card data feed and travel data feed information into expense reports, there is a need to make the data more useful and reliable. For example, it is difficult to exactly predict a car rental expense prior to a trip (e.g., a traveler may return a car with an empty tank of fuel and owe more than the base rate, a traveler may opt to use the rental company's insurance policy, a rental car may be returned early or late, and taxes may not be provided on the data feed). Hotel expenses are also difficult to predict (e.g., telephone or room service are not on the reservation, a traveler may check out early or late). In addition, a traveler often calls the vendor (e.g., car rental company, hotel) directly and changes or cancels the reservation, and that change is often not reflected in the GDS.
Air ticket information on travel data feeds is typically more accurate than hotel or car information because the ticket purchase occurs within the GDS system (whereas hotel and car payments typically occur at the end of the stay or rental) and because the GDS systems were originally built for the specific purpose of handling air ticket reservations and purchases. However, inaccuracies still arise. For example, with some refundable air tickets, the traveler can exchange the ticket at the airport for a different ticket, and this refund may not be reflected in the data available to the agency because the traveler bypassed the agency and went straight to the airline.
For these reasons, credit card data feeds, where exact costs are known, are more reliable and accurate than travel data feeds. In fact, use of only travel data feeds alone may actually decrease accuracy, as the fees in the travel data feeds are often inaccurate. If the traveler does not correct the fee, a reimbursement could be issued for an incorrect amount, causing accounting problems.
It is also useful to have expenses submitted match not only the credit card data feeds, but also the travel data feeds. If certain data matches the credit card data feed, then management reliably knows what expenses have been booked and not yet expensed, providing a good estimate of amounts owed to employees. In addition, travel data feeds often contain more information than the credit card data feeds. Access to this additional information for reporting and data-mining purposes enables management to make better business decisions.
Attempts to link credit card data feed information with travel data feed information provide many challenges. Comparing the two data feeds to each other to find exact matches does not address many real-world situations. For example, travel data feed information is typically available before a trip takes place. Credit card data feed information is typically unavailable until several days after the credit card charge has occurred. With central billing cards or “ghost cards,” the charge data is often not available until the end of a month in which the expense was incurred. Travelers often want to submit travel reports as soon as possible upon returning to expedite reimbursement. If the traveler has already created an expense report with the travel data on it and submitted it prior to the credit card data becoming available, there is a need to associate the credit card record with the already-submitted expense report. This association should be performed: to prevent duplicate submission (and possibly double-reimbursement) of the same expenses; to check the amount of the expense (travel data feeds often have inaccurate amounts, and manually-inputted expense items may contain user errors); and to link the credit card data to the expense for reporting purposes.
In addition, data feeds often contain varying levels of data quality. Some major credit card vendors charge customers for an automated data feed, with tiered rates where customers pay more for feeds with richer data and less for feeds with minimal data. Purchase dates often do not match travel dates, either because the vendor batches up several days worth of charges and submits them at once, or because the ticket or hotel room or rental was paid in advance. Credit card data feeds do not always contain travel dates. Sometimes the credit card data feeds contain merchant codes, but other times the credit card data feeds only contain the name of the merchant, creating confusion (e.g., are “Value Inn” and “Value Inn Express” the same merchant?). It is not always possible to know with absolute certainty that a given credit card charge matches a specific travel event. A mechanism is needed to judge the probability of any credit card data matching a given travel event request and to match the most probable credit card data feed record with the most probable travel data feed record.
Furthermore, travel data feeds also contain varying levels of data quality. Some GDS vendors have systems where all travel itinerary changes are transmitted on a data feed (e.g., the Galileo IDS system). Other travel feeds come from agencies and are limited to the data that the agency provides. In some cases, a travel record change is added to the travel data feed by having an agent manually make a selection or enter a code to add the change to the feed. This type of process is very prone to human error, so it is possible that the travel data feed may not contain the most up-to-date itinerary information.
In addition, the travel data known prior to the trip may not be representative of the trip actually taken. For example, the traveler may have a reservation at Hotel A, but may change that reservation to Hotel B if the traveler discovers that Hotel B is closer to a meeting site. A matching process that requires exact vendor match would never match the hotel reservation at Hotel A with the credit card charge at Hotel B the data feeds. However, as Hotel B was the hotel used on the trip, it would be useful to match the Hotel B credit card charge to the Hotel A reservation so that management knows that the Hotel A reservation will not be expensed in addition to the Hotel B credit card charge.
Additionally, travel vendors make “trusted” receipts available in digital form to their customers. These trusted receipts contain valuable data that is useful in the expense management process.